Simplifying Hyperparameter Tuning in Online Machine Learning -- The spotRiverGUI

Batch Machine Learning (BML) reaches its limits when dealing with very large amounts of streaming data. This is especially true for available memory, handling drift in data streams, and processing new, unknown data. Online Machine Learning (OML) is an alternative to BML that overcomes the limitations of BML. OML is able to process data in a sequential manner, which is especially useful for data streams. The river package is a Python OML-library, which provides a variety of online learning algorithms for classification, regression, clustering, anomaly detection, and more. The spotRiver package provides a framework for hyperparameter tuning of OML models. The spotRiverGUI is a graphical user interface for the spotRiver package. The spotRiverGUI releases the user from the burden of manually searching for the optimal hyperparameter setting. After the data is provided, users can compare different OML algorithms from the powerful river package in a convenient way and tune the selected algorithms very efficiently.